Redesign Problem Research Articles

Sampled-data controller synthesis using dissipative linear periodic jump-flow systems with design applications

In this paper, we will propose linear-matrix-inequality-based techniques for the design of sampled-data controllers that render the closed-loop system dissipative with respect to quadratic supply functions, which includes passivity and an upper-bound on the system’s H∞-norm as a special case. To arrive at these results, we model the sampled-data control system as a linear periodic jump-flow system, study dissipativity in terms of differential linear matrix inequalities (DLMIs) and then convert these DLMIs into a single linear matrix inequality. We will present three applications of these synthesis techniques: (1) passivity-based controller synthesis, as found in teleoperations, (2) input–output-response matching of a continuous-time filter with a discrete-time filter (by minimizing the H∞-norm of a generalized plant) and (3) a sampled-data controller redesign problem, where the objective is to find the best sampled-data controller, in the H∞-norm sense, for a given continuous-time controller. We will show that synthesizing sampled-data controllers leads to better closed-loop system behaviour than using a Tustin discretization of a continuous-time controller.

Decomposition heuristics for multiobjective problems. The Food bank network redesign case

Large MO-MILP problems are often very hard to solve by exact methods. In this study we consider the Food bank network redesign (FBNR) problem to introduce three decompose-and-fix heuristics that successfully solve large real-life based instances of the problem. The FBNR problem is modeled as a multi-period, multi-product supply chain redesign problem that accounts for economic, environmental and social objectives in three distinct functions. Each new heuristic decomposes the FBNR problem into two MO-MILP problems, which are sequentially solved following the lexicographic concept. Decomposition observe the nature and terms of the decisions involved. The first MO-MILP problem concerns only the longer term decisions. After the corresponding decisions are fixed, the second MO-MILP problem referring to shorter term decisions is solved respecting the ranking of objectives followed for each solution obtained in the first problem. The difference among the heuristics lies in what is considered as longer or shorter term decisions. Besides solving large instances of the problem, which the exact method used could not do, CPU time savings generated by the heuristics range from 80% to 97% of the time required by the exact method. Compromise between the computational effort and the quality of solutions obtained by each heuristic is discussed. The solving methodology proposed in this study can be adapted to other large multiobjective problems.

A novel modelling approach for the redesign of supply chains: An application to soybean grain supply chains

The redesign of an existent supply chain is a major challenge for stakeholders in the supply chain, and the supply chain redesign problem has seceral variants depending on the structure and parameters of the existent supply chain as well as the objective of the redesign. In this paper, we focus on one variant problem of the redesign of an existent two-echelon supply chain, with a view to determining the optimal supply chain redesign whose objective is the alteration of the transportation mode mix, so as to reduce the role of the road transportion mode through the introduction of an intermediate echelon of intermodal termials, and thus permittting the enhancement of the rail and inland waterways transportaion modes, which results in the reduction of operating costs and GHG emissions. Towards this end, we present a novel redesign strategy based on a four-step methodology employing bilevel programming, which is validated by application to a real world case study of a soybean exportation supply chain in Brazil. In this manner, the novel methodology presented in this paper can serve as a benchmark for supply chain redesign methods employing approximate methods.

The Effect of Laser Cutting Heuristic Presentation Modality on Design Learning

Abstract The goal of this work is to study the way student designers use heuristics to effectively design for laser-cut manufacturing methods. With the recent advent of academic makerspaces, digital fabrication tools like laser cutters are relatively new additions to the classroom. Therefore, there is a gap in formal education or training on these tools, and students can find it challenging to design effectively for them. A study was performed to investigate the way students apply heuristics to redesign laser-cut assemblies when received in different modalities. All participants were given an identical lecture on laser cutting heuristics. Then, a redesign problem was presented to students, and three different experimental groups were given the heuristics in different modalities: Text-Only, text with Visual aids, and text with Tactile aids. The novelty and quality of each of the resulting redesigns were evaluated. It was hypothesized that participants would have more difficulty interpreting and applying the Text-Only heuristics, lowering the quality of their redesigned solutions relative to the other two conditions. It was also hypothesized that participants would experience fixation caused by interacting with the tactile aids, leading to the lower novelty of their redesigned solutions relative to the other two conditions. Results showed that modality played a significant role in participants’ feelings of self-efficacy after the intervention, as well as in their understanding of laser cutter design skills when responding to quiz-style questions. However, analysis of novelty and quality showed little significant impact of the intervention and varying modalities on participants’ designs.

IDENTIFYING AND COMPARING SUBPROBLEMS IN FACTORY DESIGN PROCESSES

AbstractWhen a design team faces the problem of designing a complex system, they are required to make several decisions. Because such design problems are difficult to solve all at once, teams often decompose the design problem into several smaller subproblems. This paper discusses the results of a study designed to understand how design teams decompose a factory redesign problem into sets of related subproblems and compare the subproblems obtained for each design team. This exploratory study analyzed the design activities of eight teams of professionals and used clustering to group the variables that the design teams considered. We found that the design teams used different decomposition strategies and different subproblems, but they more often considered subproblems with design variables of the same type, and some teams followed a top-down design process.

An Intelligent Redesign Method for Used Products Based on Digital Twin

Remanufacturing used products is an important technological approach in sustainable development and circular economy. Meanwhile, redesign is the key component of remanufacturing, as it can innovate the function and structure of used products. However, due to the uncertain quality, variety, and small batches of the returned used products for remanufacturing, it is difficult to generate a sound redesign scheme to satisfy the customer demand quickly and dynamically. In addition, it is unpredictable whether the redesign scheme is suitable for the remanufacturing processes, which may lead to additional remanufacturing costs. In order to improve the efficiency of design and obtain the optimal design scheme, it is necessary to use intelligent technology to quickly generate and optimize the redesign scheme. To address this, an intelligent redesign method for used products based on the digital twin is proposed in this paper. Digital twin (DT) technology can connect the physical world with the virtual world and use the virtual model to simulate the redesign process, which is conducive to the dynamic adjustment and optimization of the redesign scheme. Firstly, the redesign process framework is constructed based on the axiomatic design (AD) method, and the redesign features of the used products are analyzed to determine the redesign problems. Then, based on the connotation of a digital twin, an intelligent redesign framework is constructed, which provides detailed guidance for building the digital-twin-driven redesign system. Henceforth, the application of the redesign process based on a digital twin is discussed, a technical approach of the digital-twin-driven redesign is proposed, and data processing methods, such as data cleaning, data integration, and data analysis, are used to realize the redesign scheme decision. Finally, the feasibility of this method is verified by the redesign of a used clutch.

Optimal trauma care network redesign with government subsidy: A bilevel integer programming approach

Trauma presents a prominent health problem worldwide. However, trauma centers are often clustered in urban areas and sparsely located in rural areas. The geographic maldistribution of trauma centers leads to system-related mistriage errors. While some local governments offer subsidy to incentivize the affiliated hospital group to redesign the trauma care network, the approach is ad hoc. To address this issue, we propose a bilevel integer programming model to investigate the subsidized trauma care network redesign problem, which considers the government as the leader and the hospital group as the follower. To solve the resultant problem efficiently, we propose a branching idea to exclude additional infeasible solutions and suboptimal solutions, in turn speeding up the branch-and-bound algorithm. In a case study, we redesign a trauma care network in the midwestern area of the U.S. based on closed-form approximate functions of system-related mistriage errors. The results show that the optimal network redesign redistributes the network by slightly reducing the number of trauma centers to relieve the crowded trauma care resource, and achieves an overall improvement of about 11% over the original network.

Assessing performance in health care: A mathematical programming approach for the re-design of primary health care networks

Mathematical models allow studying complex systems. In particular, optimal facility location models provide a sound framework to assess the performance of first-level of health care networks. In this work, a methodology founded on need/offer/demand quantification through a facility location-based mathematical model is proposed to assess the performance of existing networks of Primary Health Care Centers (PHCC) and assist in its re-design. The proposed re-design problem investigates the re-allocation of existing resources within the given infrastructure (existing PHCCs) to better satisfy the estimated health needs of the target population. This problem has not been widely addressed in the open literature despite its paramount importance in modern societies with fast demographic dynamics and constrained investment capacities. The model seeks to optimally assign the required type of service and the corresponding capacity to each PHCC (offer). The objective function to be maximized is the number of (needed) patients’ visits effectively covered by the network (demand). The following constraints are explicitly considered: i) geographic accessibility from need centers to PHCCs, ii) maximum delivery capacity of each service in each PHCC, and iii) total budget regarding fixed, variable, and relocation costs. The proposed methodology was applied to a medium-size city. Results show that the non-attended necessity can be reduced by introducing capacity modifications in the existing network. Moreover, different solutions are obtained if budgetary restrictions or minimum attention volume constraints are included. This reveals how model-based decision support tools can help health decision-makers assessing primary health care network performance.

A Bi-Objective Sustainable Supply Chain Redesign: What Effect Does Energy Availability Have on Redesign?

During supply chain redesign processes, many environmental initiatives at facilities and their surrounding regions are affected by the closing, opening, or even broadening of facilities. These initiatives may be about the surplus renewable energies that the facility generates for its region, the renewable energy sources that are available in the area for the facility, or the ecological relationships between facilities and their surrounding regions in general. The implications of these ecological relationships for the supply chain redesign problem were considered in this study in order to minimize the negative outcomes while minimizing the process’s related expenses. Thus, to minimize the unwanted effects, an objective function is defined in addition to the cost objective, which results in a bi-objective problem. Although small cases can be solved by exact methods such as AUGMECON, for larger scales, a matheuristic algorithm needs to be developed. The efficiency of the developed algorithm was investigated through numerical instances as well as with different metrics including a Pareto analysis. The results at larger scales show a shorter runtime of the matheuristic compared to the AUGMECON algorithm. Additionally, the matheuristic method provides a wider range of decision areas for managers.

Supply chain network redesign problem for major beverage organization in ASEAN region

Supply chain network redesign problem for major beverage organization in ASEAN region

THE RELATIONSHIP BETWEEN FUNCTIONS AND OUTCOMES OF BIOLOGICALLY-INSPIRED DESIGN

AbstractResearch in biologically-inspired design (BID) practice often focus on team composition or ideation based on an already discovered fascinating biological solution principle. However, how are the outcome of the early design phases affecting BID projects' quality?In this study, historical data from 91 reports from student teams documenting their BID efforts from a 3-week course constitute the data source. Thus, the relationship between design problem types, function types, functions descriptions and BID projects' quality is addressed.The study show that especially design problem types and function descriptions affect the BID projects' quality. For instance, BID projects dealing with open-ended problems yield better results than redesign problems with existing solutions operating in a very domain-limited solution space. Next, BID projects obtain the best results when using functions as drivers for analogy searching rather than properties. Finally, BID projects with certain function types seem to have more complicated conceptualization phases.

Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

Solving a Real-World Urban Postal Service System Redesign Problem

Due to recent technological advancements, more diversified customer demand, and increasingly harder competition, traditional postal service systems have experienced significant changes all over the world. In Norway, through a strategic reform called post-in-shop, undertaken in 2013, most postal services are now provided at postal service counters located in retailer stores in order to improve accessibility, operational efficiency, and cost-effectiveness. This has led to a complex decision-making problem for the redesign of urban postal service networks across the country. In this paper, a two-stage method is proposed to solve a real-world urban postal service network redesign problem. First, two location models are employed to determine the optimal locations of postal service counters. In the second stage, a simulation model is built to evaluate the urban postal service system with different location and demand allocation plans under a realistic and stochastic environment. Among other insights, our results show that the proposed two-stage method can be used to effectively improve the accessibility of postal service networks by making optimal location-allocation decisions.

Finding diverse ways to improve algebraic connectivity through multi-start optimization

AbstractThe algebraic connectivity, also known as the Fiedler value, is a spectral measure of network connectivity that can be increased through edge addition. We present an algorithm for producing many diverse ways to add a fixed number of edges to a network to achieve a near optimal Fiedler value. Previous Fielder value optimization algorithms (i.e. the greedy algorithm) output only one solution. Obtaining a single solution is rarely good enough for real-world network redesign problems, as practical constraints (political, physical or financial) may prevent implementation. Our algorithm takes a multi-start optimization approach, adding a random initial edge and then applies a greedy heuristic to improve the Fiedler value. The random choice moves us to a new region of the search space, enabling discovery of diverse solutions. Additionally, we present a Determinantal Point Process framework for quantifying diversity. We then apply a Markov chain Monte Carlo technique to sift through the large number of output solutions and locate a smaller, more manageable collection of highly diverse solutions that can be presented to network redesign engineers. We demonstrate the effectiveness of our algorithm on real-world graphs with varied structures.

Inventory Risk Pooling Strategy for the Food Distribution Network in Korea

Today's business environments are characterised by the high degree of volatility and risk due to fast changing customer behaviours and increasingly diversified product lines. Since the volatility and risk are often triggered by demand variability, growing efforts are made to control demand variability. One of such efforts includes inventory risk pooling which aims to reduce inventory variability by aggregating customer demand across products, time, and location. The success of inventory risk pooling, however, hinges on its ability to consolidate inventory at the central location of a supply chain network. To help supply chain professionals formulate a wise inventory risk pooling strategy, this paper redesigns a warehouse network in such a way that it increases inventory turnover, reduces the risk of inventory obsolescence/shortage/surplus, allocates inventory to field warehouses closer to highly concentrated customer bases, and centralises inventory stocking locations for the aggregated demand. To solve this complex distribution network redesign problem, we propose a simulation model and test its validity by applying it to an actual distribution problem encountering the consumer packaged goods manufacturer in Korea, which produces and distributes food products. [Received: 14 August 2017; Accepted: 30 June 2020]

A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign

This paper aims to develop a scenario-based optimization framework to deals with several issues related to redesign sustainable reverse supply chain; focused particularly in the epistemic uncertainty of supply and demand input parameters and their relationship with supply chain performance, both important for the redesign problem. The two-step optimization framework starts with a Fuzzy Inference System methodology for scenario generation that faces the lack of information, and the necessity of estimate the expected operation cost and environmental impact. Then we use generated scenarios into the epsilon-constraint method which solves a multi-objective model to obtain a relevant set of solutions. After solving the second optimization model, we propose to analyze the robustness of the achieved redesign solutions considering a customer satisfaction approach. The computational experiments show that our proposed framework supports better the inclusion of scenarios for redesigning the plastic recycling supply chain. Furthermore, we study a real-life plastic recycling problem in Cuba which demonstrates that the framework is able to support the redesign decision making with robust solutions sensitive to the changes of the studied uncertain parameters.

Examining the effect of design for additive manufacturing rule presentation on part redesign quality

The main goal of this research is to understand how design for additive manufacturing (DfAM) rule presentation affects a designer’s ability to utilise those rules. To that end a pair of studies were carried out. The first study was conducted with industry engineers and designers, while the second study was conducted with students at a university. For both studies, four DfAM design rules for fused deposition modelling (FDM) were chosen, relating to overhangs, planar surfaces, accessible support structures, and part size. Each rule was presented in four different modalities: text only, text with illustration, text with industry example, and text with 3D printed example. Each rule presentation included a justification, and all but the text-only presentation included a ‘desirable’ and ‘undesirable’ design example for the rule. Four part redesign problems were given, and the resulting redesigns were then rated on both novelty and quality. Results indicate that although there are no differences in quality and novelty scores between modalities, the text only rules were perceived to be the most difficult to understand. Furthermore, a comparison between the professionals and the students showed that the professionals created higher novelty redesigns. These results have several implications in the field of DfAM education.

Alternative proposal, based on systems-engineering methods, aimed at substituting with carbon-epoxy laminates the load-bearing aluminum sandwiches employed in the structure of a small satellite

The development process of composite structures has suffered from a lack of balance between analysis, design and the use of design methods. This study finds a solution to this matter with an alternative proposal that aims at substituting with a stiffer and lighter material the aluminum alloy originally employed in the construction of the primary structure of a micro satellite (50 kg). The structural components to be substituted are the four vertical lateral walls (VLWs) fabricated as honeycomb sandwich structures (SS). The alternative structure consists of a carbon fiber-epoxy (C-Ep) laminate whose stacking sequence is chosen so it is configured as quasi-isotropic, balanced, regular, homogeneous and symmetrical. The alternative proposal is justified from the standpoint of the feasibility of C-Ep in space applications; the reduction of the launch cost; and the enhancement of the structural performance. The first aspect is verified with a state-of-the-art review. Launch cost reduction is estimated conjecturally based on launch prices of the Ariane family of rockets. The development process of the C-Ep laminate is carried out from start to finish with an integral design process based on well-known systems-engineering (SE) methods and a structural analysis based on classic analytic and numerical approaches of thin-wall aeronautical structures. The methodological approach draws upon the Quality Functions Deployment (QFD) and the Functional Analysis (FA). Both methods allow to understand better the redesign problem and to propose a solution that ensures the satisfaction of the customer needs (i.e., launcher, structural designers, mission). The fabrication of the C-Ep laminate is accomplished with the wet lay-up process plus hot bonding with flexible heater blankets and a vacuum bag. The choice of C-Ep as the main construction material implied a reduction of 40% of mass, and a simultaneous increase of almost 12 times the bending stiffness, 4 times the buckling strength and 10 times the buckling load with respect to the all-metal VLW. Launch cost reduction could be substantial depending on the initial cost. To conclude, the hypothesis that conjectures a reduction of costs and the enhancement of the performance of the structure is proved positive on account of the simultaneous increase of the specific stiffness of the material and of the load bearing capacity of the C-Ep VLW. QFD and FA prove to be effective methods to understand and solve the redesign problem beyond the standard methods focused exclusively on analysis

Deep Dive into Machine Learning Models for Protein Engineering.

Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification steps in the laboratory environment. For any given protein, the number of possible mutations is astronomical. It is impractical to synthesize all sequences or even to investigate all functionally interesting variants. Recently, there has been an increased interest in using machine learning to assist protein redesign, since prediction models can be used to virtually screen a large number of novel sequences. However, many state-of-the-art machine learning models, especially deep learning models, have not been extensively explored. Moreover, only a small selection of protein sequence descriptors has been considered. In this work, the performance of prediction models built using an array of machine learning methods and protein descriptor types, including two novel, single amino acid descriptors and one structure-based three-dimensional descriptor, is benchmarked. The predictions were evaluated on a diverse collection of public and proprietary data sets, using a variety of evaluation metrics. The results of this comparison suggest that Convolution Neural Network models built with amino acid property descriptors are the most widely applicable to the types of protein redesign problems faced in the pharmaceutical industry.

A DSS-Based Novel Approach Proposition Employing Decision Techniques for System Design

In this study, to enhance flexibility and agility, a special DSS is developed for a system re-design problem, while a decision-making technique is employed afterwards to enable the accelerated and reliable results, as a progressive approach to close a gap of related literature. Proposed approach generates all possible machine schedules regarding some tangible traditional constraints, after that, generated schedules are ranked by ORESTE method regarding human related linguistic variables and ergonomic factors this time, with the aim of harvesting the best outcome. Thus, all traditional and newly introduced problem constraints could be optimized in the best schedule, while time and effort sources of organisation are preserved by employment of proposed DSS, mathematical modelling, and ORESTE. A numerical application of proposed approach is executed and mechanism of proposed novel model is explained in detail as well as usability and salutary of the approach is proved with real life data.